"""
Colour Matching Functions Transformations
=========================================
Define various educational objects for colour matching functions
transformations:
- :func:`colour.colorimetry.RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs`
- :func:`colour.colorimetry.RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs`
- :func:`colour.colorimetry.RGB_10_degree_cmfs_to_LMS_10_degree_cmfs`
- :func:`colour.colorimetry.LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs`
- :func:`colour.colorimetry.LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs`
References
----------
- :cite:`CIETC1-362006a` : CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental
Chromaticity Diagram with Physiological Axes - Part 1. Commission
Internationale de l'Eclairage. ISBN:978-3-901906-46-6
- :cite:`CVRLp` : CVRL. (n.d.). CIE (2012) 10-deg XYZ
"physiologically-relevant" colour matching functions. Retrieved June 25,
2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm
- :cite:`CVRLv` : CVRL. (n.d.). CIE (2012) 2-deg XYZ
"physiologically-relevant" colour matching functions. Retrieved June 25,
2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm
- :cite:`Wyszecki2000be` : Wyszecki, Günther, & Stiles, W. S. (2000). The
CIE 1964 Standard Observer. In Color Science: Concepts and Methods,
Quantitative Data and Formulae (p. 141). Wiley. ISBN:978-0-471-39918-6
- :cite:`Wyszecki2000bg` : Wyszecki, Günther, & Stiles, W. S. (2000). Table
1(3.3.3). In Color Science: Concepts and Methods, Quantitative Data and
Formulae (pp. 138-139). Wiley. ISBN:978-0-471-39918-6
"""
from __future__ import annotations
import numpy as np
from colour.algebra import vecmul
from colour.colorimetry import (
MSDS_CMFS_LMS,
MSDS_CMFS_RGB,
SDS_LEFS_PHOTOPIC,
reshape_sd,
)
from colour.hints import ArrayLike, NDArrayFloat
from colour.utilities import tstack
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs",
"RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs",
"RGB_10_degree_cmfs_to_LMS_10_degree_cmfs",
"LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs",
"LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs",
]
[docs]
def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(
wavelength: ArrayLike,
) -> NDArrayFloat:
"""
Convert *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions
into the *CIE 1931 2 Degree Standard Observer* colour matching functions.
Parameters
----------
wavelength
Wavelength :math:`\\lambda` in nm.
Returns
-------
:class:`numpy.ndarray`
*CIE 1931 2 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 1931 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
:cite:`Wyszecki2000bg`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0113577..., 0.004102 , 0. ])
"""
cmfs = MSDS_CMFS_RGB["Wright & Guild 1931 2 Degree RGB CMFs"]
rgb_bar = cmfs[wavelength]
rgb = rgb_bar / np.sum(rgb_bar)
M1 = np.array(
[
[0.49000, 0.31000, 0.20000],
[0.17697, 0.81240, 0.01063],
[0.00000, 0.01000, 0.99000],
]
)
M2 = np.array(
[
[0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063],
]
)
xyz = vecmul(M1, rgb) / vecmul(M2, rgb)
x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2]
V = reshape_sd(
SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"],
cmfs.shape,
copy=False,
)
L = V[wavelength]
x_bar = x / y * L
y_bar = L
z_bar = z / y * L
xyz_bar = tstack([x_bar, y_bar, z_bar])
return xyz_bar
[docs]
def RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(
wavelength: ArrayLike,
) -> NDArrayFloat:
"""
Convert *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *CIE 1964 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength
Wavelength :math:`\\lambda` in nm.
Returns
-------
:class:`numpy.ndarray`
*CIE 1964 10 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 1964 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
:cite:`Wyszecki2000be`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0096432..., 0.0037526..., -0.0000041...])
"""
cmfs = MSDS_CMFS_RGB["Stiles & Burch 1959 10 Degree RGB CMFs"]
rgb_bar = cmfs[wavelength]
M = np.array(
[
[0.341080, 0.189145, 0.387529],
[0.139058, 0.837460, 0.073316],
[0.000000, 0.039553, 2.026200],
]
)
xyz_bar = vecmul(M, rgb_bar)
return xyz_bar
[docs]
def RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(
wavelength: ArrayLike,
) -> NDArrayFloat:
"""
Convert *Stiles & Burch 1959 10 Degree RGB CMFs* colour matching
functions into the *Stockman & Sharpe 10 Degree Cone Fundamentals*
spectral sensitivity functions.
Parameters
----------
wavelength
Wavelength :math:`\\lambda` in nm.
Returns
-------
:class:`numpy.ndarray`
*Stockman & Sharpe 10 Degree Cone Fundamentals* spectral tristimulus
values.
Notes
-----
- Data for the *Stockman & Sharpe 10 Degree Cone Fundamentals* already
exists, this definition is intended for educational purpose.
References
----------
:cite:`CIETC1-362006a`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0052860..., 0.0003252..., 0. ])
"""
cmfs = MSDS_CMFS_RGB["Stiles & Burch 1959 10 Degree RGB CMFs"]
rgb_bar = cmfs[wavelength]
M = np.array(
[
[0.1923252690, 0.749548882, 0.0675726702],
[0.0192290085, 0.940908496, 0.113830196],
[0.0000000000, 0.0105107859, 0.991427669],
]
)
lms_bar = vecmul(M, rgb_bar)
lms_bar[..., -1][np.asarray(np.asarray(wavelength) > 505)] = 0
return lms_bar
[docs]
def LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(
wavelength: ArrayLike,
) -> NDArrayFloat:
"""
Convert *Stockman & Sharpe 2 Degree Cone Fundamentals* colour matching
functions into the *CIE 2015 2 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength
Wavelength :math:`\\lambda` in nm.
Returns
-------
:class:`numpy.ndarray`
*CIE 2015 2 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2015 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
:cite:`CVRLv`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0109677..., 0.0041959..., 0. ])
"""
cmfs = MSDS_CMFS_LMS["Stockman & Sharpe 2 Degree Cone Fundamentals"]
lms_bar = cmfs[wavelength]
M = np.array(
[
[1.94735469, -1.41445123, 0.36476327],
[0.68990272, 0.34832189, 0.00000000],
[0.00000000, 0.00000000, 1.93485343],
]
)
xyz_bar = vecmul(M, lms_bar)
return xyz_bar
[docs]
def LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(
wavelength: ArrayLike,
) -> NDArrayFloat:
"""
Convert *Stockman & Sharpe 10 Degree Cone Fundamentals* colour matching
functions into the *CIE 2015 10 Degree Standard Observer* colour matching
functions.
Parameters
----------
wavelength
Wavelength :math:`\\lambda` in nm.
Returns
-------
:class:`numpy.ndarray`
*CIE 2015 10 Degree Standard Observer* spectral tristimulus values.
Notes
-----
- Data for the *CIE 2015 10 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
:cite:`CVRLp`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0098162..., 0.0037761..., 0. ])
"""
cmfs = MSDS_CMFS_LMS["Stockman & Sharpe 10 Degree Cone Fundamentals"]
lms_bar = cmfs[wavelength]
M = np.array(
[
[1.93986443, -1.34664359, 0.43044935],
[0.69283932, 0.34967567, 0.00000000],
[0.00000000, 0.00000000, 2.14687945],
]
)
xyz_bar = vecmul(M, lms_bar)
return xyz_bar
